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<a href="_ensemble_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Implements the Ensemble Model that can be used to merge predictions from weighted models</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \author      O. Krause</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * \date        2018</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> *</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * </span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * </span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * </span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * </span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> *</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> */</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span> </div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#ifndef SHARK_MODELS_ENSEMBLE_H</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#define SHARK_MODELS_ENSEMBLE_H</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_model_8h.html">shark/Models/AbstractModel.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#include &lt;<a class="code" href="_classifier_8h.html">shark/Models/Classifier.h</a>&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#include &lt;type_traits&gt;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>    </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="keyword">namespace </span>detail{</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> BaseModelType, <span class="keyword">class</span> VectorType&gt;</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="keyword">class </span>EnsembleImpl: <span class="keyword">public</span> AbstractModel&lt;</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    typename std::remove_pointer&lt;BaseModelType&gt;::type::InputType,</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>    VectorType,</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    typename std::remove_pointer&lt;BaseModelType&gt;::type::ParameterVectorType</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>&gt;{</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> std::remove_pointer&lt;BaseModelType&gt;::type ModelType;</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    <span class="keyword">typedef</span> AbstractModel&lt;typename ModelType::InputType, VectorType, typename ModelType::ParameterVectorType&gt; Base;</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span> </div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>    <span class="comment">// the following functions are returning a reference to the model</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    <span class="comment">// independent of whether a pointer to the model or the model itself </span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    <span class="comment">// is stored.</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    ModelType&amp; derefIfPtr(ModelType&amp; model)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        <span class="keywordflow">return</span> model;</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    }</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    ModelType <span class="keyword">const</span>&amp; derefIfPtr(ModelType <span class="keyword">const</span>&amp; model)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        <span class="keywordflow">return</span> model;</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    }</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    ModelType&amp; derefIfPtr(ModelType* model)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        <span class="keywordflow">return</span> *model;</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    }</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>    </div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span> </div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    <span class="comment">//implements the pooling operation which creates a vector from the model responses to the given patterns</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> T&gt; <span class="keyword">struct </span><a class="code hl_namespace" href="namespacetag.html">tag</a>{};</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span> </div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> InputBatch, <span class="keyword">class</span> T, <span class="keyword">class</span> Device&gt;</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    <span class="keywordtype">void</span> pool(InputBatch <span class="keyword">const</span>&amp; patterns, blas::matrix&lt;T, blas::row_major, Device&gt;&amp; outputs, <a class="code hl_namespace" href="namespacetag.html">tag</a>&lt;blas::vector&lt;T, Device&gt; &gt;)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != numberOfModels(); i++){</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>            noalias(outputs) += weight(i) * model(i)(patterns);</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        }</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        outputs /= <a class="code hl_function" href="namespaceshark.html#ad53c908307c4f68be02eb87dad27a608" title="Returns the total sum of weights.">sumOfWeights</a>();</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    }</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> InputBatch, <span class="keyword">class</span> OutputBatch&gt;</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    <span class="keywordtype">void</span> pool(InputBatch <span class="keyword">const</span>&amp; patterns, OutputBatch&amp; outputs, tag&lt;unsigned int&gt;)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>        blas::vector&lt;unsigned int&gt; responses;</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != numberOfModels(); ++i){</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>            model(i).eval(patterns, responses);</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>            <span class="keywordflow">for</span>(std::size_t p = 0; p != patterns.size1(); ++p){</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>                outputs(p,responses(p)) += weight(i);</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>            }</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        }</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        outputs /= <a class="code hl_function" href="namespaceshark.html#ad53c908307c4f68be02eb87dad27a608" title="Returns the total sum of weights.">sumOfWeights</a>();</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    }</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span> </div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    std::vector&lt;BaseModelType&gt; m_models;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    RealVector m_weights;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#a518304e95092673b7b6438cace052ef6" title="defines the batch type of the input type.">Base::BatchInputType</a> BatchInputType;</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_model.html#aa0c72e230b9a1324c95ba8ac0b07ba13" title="defines the batch type of the output type">Base::BatchOutputType</a> BatchOutputType;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_i_parameterizable.html#a2ad5e2e60b2b352988b41f46024d790b">Base::ParameterVectorType</a> <a class="code hl_typedef" href="classshark_1_1_i_parameterizable.html#a2ad5e2e60b2b352988b41f46024d790b">ParameterVectorType</a>;</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span> </div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    ParameterVectorType <a class="code hl_function" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db" title="Return the parameter vector.">parameterVector</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        <span class="keywordflow">return</span> {};</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    }</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(ParameterVectorType <span class="keyword">const</span>&amp; param) {</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(param.size() == 0);</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    }</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span> </div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    <span class="keywordtype">void</span> addModel(BaseModelType <span class="keyword">const</span>&amp; model, <span class="keywordtype">double</span> weight = 1.0){</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(weight &gt; 0, <span class="stringliteral">&quot;Weights must be positive&quot;</span>);</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        m_models.push_back(model);</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        m_weights.push_back(weight);</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>    }</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    <span class="comment"></span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment">    /// \brief Removes all models from the ensemble</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment"></span>    <span class="keywordtype">void</span> clearModels(){</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        m_models.clear();</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        m_weights.clear();</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>    }</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>    </div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    ModelType&amp; model(std::size_t index){</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        <span class="keywordflow">return</span> derefIfPtr(m_models[index]);</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    }</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    </div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>    ModelType <span class="keyword">const</span>&amp; model(std::size_t index)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        <span class="keywordflow">return</span> derefIfPtr(m_models[index]);</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    }</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    <span class="comment"></span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment">    /// \brief Returns the weight of the i-th model.</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment"></span>    <span class="keywordtype">double</span> <span class="keyword">const</span>&amp; weight(std::size_t i)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        <span class="keywordflow">return</span> m_weights[i];</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    }</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    <span class="comment"></span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    /// \brief Returns the weight of the i-th model.</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment"></span>    <span class="keywordtype">double</span>&amp; weight(std::size_t i){</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        <span class="keywordflow">return</span> m_weights[i];</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>    }</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    <span class="comment"></span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment">    /// \brief Returns the total sum of weights used for averaging</span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="namespaceshark.html#ad53c908307c4f68be02eb87dad27a608" title="Returns the total sum of weights.">sumOfWeights</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="keywordflow">return</span> sum(m_weights);</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>    }</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>    <span class="comment"></span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">    /// \brief Returns the number of models.</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span><span class="comment"></span>    std::size_t numberOfModels()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        <span class="keywordflow">return</span> m_models.size();</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    }</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>    <span class="comment"></span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="comment">    ///\brief Returns the expected shape of the input</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span><span class="comment"></span>    Shape inputShape()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>        <span class="keywordflow">return</span> m_models.empty() ? Shape(): model(0).inputShape();</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>    }<span class="comment"></span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span><span class="comment">    ///\brief Returns the shape of the output</span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span><span class="comment"></span>    Shape outputShape()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        <span class="keywordflow">return</span> m_models.empty() ? Shape(): model(0).outputShape();</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    }</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span> </div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>    <span class="keyword">using </span><a class="code hl_function" href="classshark_1_1_abstract_model.html#ac7edef74da55322b6aef0ba65b08592d" title="Standard interface for evaluating the response of the model to a batch of patterns.">Base::eval</a>;</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>    <span class="keywordtype">void</span> eval(BatchInputType <span class="keyword">const</span>&amp; patterns, BatchOutputType&amp; outputs)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        outputs.resize(patterns.size1(), outputShape().numElements());</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        outputs.clear();</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        pool(patterns,outputs, tag&lt;typename ModelType::OutputType&gt;());</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    }</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    <span class="keywordtype">void</span> eval(BatchInputType <span class="keyword">const</span>&amp; patterns, BatchOutputType&amp; outputs, State&amp;)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        eval(patterns,outputs);</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>    }</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>    </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_i_serializable.html#ad4ad9a7c274deff642f91e98417fbc63" title="Read the component from the supplied archive.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a>&amp; archive){</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        std::size_t numModels;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        archive &gt;&gt; numModels;</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        m_models.resize(numModels);</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != numModels; ++i){</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>            archive &gt;&gt; model(i);</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        }</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        archive &gt;&gt; m_weights;</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    }</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_i_serializable.html#a9bddedd42933c922e323b73131f62f12" title="Write the component to the supplied archive.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a>&amp; archive)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        std::size_t numModels = m_models.size();</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        archive &lt;&lt; numModels;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != numModels; ++i){</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>            archive &lt;&lt; model(i);</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        }</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        archive &lt;&lt; m_weights;</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>    }</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>};</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span> </div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="comment">//the following creates an ensemble base depending on whether the ensemble should be a classifier or not.</span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span> </div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> ModelType, <span class="keyword">class</span> OutputType&gt;</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="keyword">struct </span>EnsembleBase : <span class="keyword">public</span> detail::EnsembleImpl&lt;ModelType, OutputType&gt;{</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> std::remove_pointer&lt;ModelType&gt;::type::OutputType ModelOutputType;</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    detail::EnsembleImpl&lt;ModelType, OutputType&gt;&amp; impl(){ <span class="keywordflow">return</span> *<span class="keyword">this</span>;};</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    detail::EnsembleImpl&lt;ModelType, OutputType&gt; <span class="keyword">const</span>&amp; impl()<span class="keyword"> const</span>{ <span class="keywordflow">return</span> *<span class="keyword">this</span>;};</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>};</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span> </div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span><span class="comment">//if the output type is unsigned int, this is a classifier</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> BaseModelType&gt;</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span><span class="keyword">struct </span>EnsembleBase&lt;BaseModelType, unsigned int&gt;</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>: <span class="keyword">public</span> Classifier&lt;detail::EnsembleImpl&lt;BaseModelType, typename std::remove_pointer&lt;BaseModelType&gt;::type::ParameterVectorType&gt; &gt;{</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> std::remove_pointer&lt;BaseModelType&gt;::type::ParameterVectorType PoolingVectorType;</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>    detail::EnsembleImpl&lt;BaseModelType, PoolingVectorType&gt;&amp; impl()</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>    { <span class="keywordflow">return</span> this-&gt;decisionFunction();}</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>    detail::EnsembleImpl&lt;BaseModelType, PoolingVectorType&gt; <span class="keyword">const</span>&amp; impl()<span class="keyword"> const</span></div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> this-&gt;decisionFunction();}</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>};</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span> </div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span><span class="comment">//if the OutputType is void, this is treated as choosing it as the OutputType of the model</span></div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> ModelType&gt;</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span><span class="keyword">struct </span>EnsembleBase&lt;ModelType, void&gt;</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>: <span class="keyword">public</span> EnsembleBase&lt;ModelType, typename std::remove_pointer&lt;ModelType&gt;::type::OutputType&gt;{};</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>}</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment"></span> </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment">/// \brief Represents en weighted ensemble of models. </span></div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span><span class="comment">///</span></div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="comment">/// In an ensemble, each model computes a response for an input independently. The responses are then pooled</span></div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span><span class="comment">/// to form a single label. The hope is that models in an ensemble do not produce the same type of errors</span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span><span class="comment">/// and thus the averaged response is more reliable. An example for this is AdaBoost, where a series</span></div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span><span class="comment">/// of weak models is trained and weighted to create one final prediction. </span></div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span><span class="comment">///</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span><span class="comment">/// There are two orthogonal aspects to consider in the Ensemble. The pooling function, which is chosen</span></div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment">/// based on the output type of the ensemble models, and the mapping of the output of the pooling function</span></div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span><span class="comment">/// to the model output.</span></div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span><span class="comment">/// </span></div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span><span class="comment">/// If the ensemble consists of models returning vectors, pooling is implemented</span></div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span><span class="comment">/// using weighted averaging. If the models return class labels, those are first transformed</span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="comment">/// into a one-hot encoding before averaging. Thus the output can be interpreted</span></div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span><span class="comment">/// as the probability of a class label when picking a member of the emsemble randomly with probability </span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment">/// proportional to its weights. </span></div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span><span class="comment">///</span></div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span><span class="comment">/// The final mapping to the output is based on the OutputType template parameter, which by default</span></div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span><span class="comment">/// is the same as the output type of the model. If it is unsigned int, the Ensemble is treated as Classifier</span></div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span><span class="comment">/// with decision function being the result of the pooling function (i.e. the class with maximum response in</span></div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span><span class="comment">/// the weighted average is chosen). In this case, Essemble is derived from Classifier&lt;&gt;. </span></div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span><span class="comment">/// Otherwise the weighted average is returned.</span></div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span><span class="comment">///</span></div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span><span class="comment">/// Note that there is a decision in algorihm design tot ake for classifiers:</span></div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span><span class="comment">/// We can either let each member of the Ensemble predict</span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span><span class="comment">/// a class-label and then pool the labels as described above, or we can create an ensemble of</span></div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="comment">/// decision functions and weight them into one decision function to produce the class-label.</span></div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span><span class="comment">/// Those approaches will lead to different results. For example if the underlying models</span></div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span><span class="comment">/// produce class probabilities, the class with the largest average probability</span></div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span><span class="comment">/// might not be the same as the class with most votes from the individual models.</span></div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span><span class="comment">///</span></div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span><span class="comment">/// Models are added using addModel.</span></div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span><span class="comment">/// The ModelType is allowed to be either a concrete model like LinearModel&lt;&gt;, in which</span></div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span><span class="comment">/// case a copy of each added model is stored. If the ModelType is a pointer, for example</span></div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span><span class="comment">/// AbstractModel&lt;...&gt;*, only pointers are stored and all added models</span></div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span><span class="comment">/// must outlive the lifetime of the ensemble. This also entails differences in serialization.</span></div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span><span class="comment">/// In the first case, the model can be serialized completely without any setup. In the second</span></div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span><span class="comment">/// case before deserializing, the models must be constructed and added.</span></div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span><span class="comment">///</span></div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span><span class="comment">/// \ingroup models</span></div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> ModelType, <span class="keyword">class</span> OutputType  = <span class="keywordtype">void</span>&gt;</div>
<div class="foldopen" id="foldopen00252" data-start="{" data-end="};">
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html">  252</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_ensemble.html" title="Represents en weighted ensemble of models.">Ensemble</a>: <span class="keyword">public</span> detail::EnsembleBase&lt;ModelType, OutputType&gt;{</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span><span class="keyword">public</span>:</div>
<div class="foldopen" id="foldopen00254" data-start="{" data-end="}">
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#a3508e39c8cbf40de08dcd387670a8f58">  254</a></span>    std::string <a class="code hl_function" href="classshark_1_1_ensemble.html#a3508e39c8cbf40de08dcd387670a8f58" title="returns the name of the object">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;Ensemble&quot;</span>; }</div>
</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>    <span class="comment"></span></div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span><span class="comment">    /// \brief Adds a new model to the ensemble.</span></div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span><span class="comment">    /// \param model the new model</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span><span class="comment">    /// \param weight weight of the model. must be &gt; 0</span></div>
<div class="foldopen" id="foldopen00261" data-start="{" data-end="}">
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#aaa96d4139d33b84477f8a41d9d12c8bb">  261</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_ensemble.html#aaa96d4139d33b84477f8a41d9d12c8bb" title="Adds a new model to the ensemble.">addModel</a>(ModelType <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49" title="Returns a reference to the i-th model.">model</a>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311" title="Returns the weight of the i-th model.">weight</a> = 1.0){</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        this-&gt;impl().addModel(<a class="code hl_function" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49" title="Returns a reference to the i-th model.">model</a>,<a class="code hl_function" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311" title="Returns the weight of the i-th model.">weight</a>);</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>    }</div>
</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>    <span class="comment"></span></div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span><span class="comment">    /// \brief Removes all models from the ensemble</span></div>
<div class="foldopen" id="foldopen00266" data-start="{" data-end="}">
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#ab04ccf3f9acb405f3fdedd125ff5ed1c">  266</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_ensemble.html#ab04ccf3f9acb405f3fdedd125ff5ed1c" title="Removes all models from the ensemble.">clearModels</a>(){</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>        this-&gt;impl().clearModels();</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>    }</div>
</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>    <span class="comment"></span></div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span><span class="comment">    /// \brief Returns the number of models.</span></div>
<div class="foldopen" id="foldopen00271" data-start="{" data-end="}">
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#a1e09f390e1605b270f0ec17b7845e472">  271</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_ensemble.html#a1e09f390e1605b270f0ec17b7845e472" title="Returns the number of models.">numberOfModels</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        <span class="keywordflow">return</span> this-&gt;impl().numberOfModels();</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>    }</div>
</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>    <span class="comment"></span></div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span><span class="comment">    /// \brief Returns a reference to the i-th model.</span></div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span><span class="comment">    /// \param i model index.</span></div>
<div class="foldopen" id="foldopen00278" data-start="{" data-end="}">
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49">  278</a></span><span class="comment"></span>    <span class="keyword">typename</span> std::remove_pointer&lt;ModelType&gt;::type&amp; <a class="code hl_function" href="classshark_1_1_ensemble.html#ade2b84f506a9ca05c99b048d89a81e49" title="Returns a reference to the i-th model.">model</a>(std::size_t i){</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>        <span class="keywordflow">return</span> this-&gt;impl().model(i);</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span><span class="comment">    /// \brief Returns a const reference to the i-th model.</span></div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span><span class="comment">    /// \param i model index.</span></div>
<div class="foldopen" id="foldopen00284" data-start="{" data-end="}">
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#a9ca0daef198afeb7c77203e30c80d52e">  284</a></span><span class="comment"></span>    <span class="keyword">typename</span> std::remove_pointer&lt;ModelType&gt;::type <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_ensemble.html#a9ca0daef198afeb7c77203e30c80d52e" title="Returns a const reference to the i-th model.">model</a>(std::size_t i)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>        <span class="keywordflow">return</span> this-&gt;impl().model(i);</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>    }</div>
</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>    <span class="comment"></span></div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span><span class="comment">    /// \brief Returns the weight of the i-th model.</span></div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span><span class="comment">    /// \param i model index.</span></div>
<div class="foldopen" id="foldopen00291" data-start="{" data-end="}">
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311">  291</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_ensemble.html#a8362fb3624b7976c8260cb5c1e98f311" title="Returns the weight of the i-th model.">weight</a>(std::size_t i)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>        <span class="keywordflow">return</span> this-&gt;impl().weight(i);</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>    }</div>
</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>    <span class="comment"></span></div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span><span class="comment">    /// \brief Returns the weight of the i-th model.</span></div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span><span class="comment">    /// \param i model index.</span></div>
<div class="foldopen" id="foldopen00298" data-start="{" data-end="}">
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#a2b8b29b5b1749b42ed5cd79a6aab7952">  298</a></span><span class="comment"></span>    <span class="keywordtype">double</span>&amp; <a class="code hl_function" href="classshark_1_1_ensemble.html#a2b8b29b5b1749b42ed5cd79a6aab7952" title="Returns the weight of the i-th model.">weight</a>(std::size_t i){</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>        <span class="keywordflow">return</span> this-&gt;impl().weight(i);</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>    }</div>
</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>    <span class="comment"></span></div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span><span class="comment">    /// \brief Returns the total sum of weights used for averaging</span></div>
<div class="foldopen" id="foldopen00303" data-start="{" data-end="}">
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno"><a class="line" href="classshark_1_1_ensemble.html#a6965c4363321584b44389670044d24bd">  303</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_ensemble.html#a6965c4363321584b44389670044d24bd" title="Returns the total sum of weights used for averaging.">sumOfWeights</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>        <span class="keywordflow">return</span> this-&gt;impl().sumOfWeights();</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>    }</div>
</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span>    </div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>};</div>
</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span> </div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>}</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span><span class="preprocessor">#endif</span></div>
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